ArticleCartilage
Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images.
Article in Cartilage. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Computer Vision in Lower Limb Orthopaedics: A Scoping Review of Imaging-Based Artificial Intelligence Applications.Cureus · 2025Review
- The Influence of Knee Position on Ultrasound Imaging of Femoral Cartilage in Individuals with Anterior Cruciate Ligament Reconstruction.Cartilage · 2024Article
- Femoral cartilage ultrasound echo-intensity is a valid measure of cartilage composition.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2024Article
- Article
- Applications of artificial intelligence in musculoskeletal ultrasound: narrative review.Frontiers in medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL).
designWe recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant's ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral cartilage cross-sectional area in each image. In addition, we created a semi-automatic program to segment the cartilage using a random walker-based method. We quantified the average cartilage thickness and echo-intensity for the manual and semi-automated segmentations. Intraclass correlation coefficients (ICC
resultsFor average cartilage thickness, there was excellent reliability (ICC
conclusionsOur novel semi-automated segmentation technique is a valid method that requires less technical expertise and time than manual segmentation in patients after ACL injury.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.